Unit 1: Introduction

 Purpose of Database Systems

 View of Data
 Data Models
 Data Definition Language
 Data Manipulation Language
 Transaction Management
 Storage Management
 Database Administrator
 Database Users
 Overall System Structure
Database Management System (DBMS)
 Collection of interrelated data
 Set of programs to access the data
 DBMS contains information about a particular
enterprise
 DBMS provides an environment that is both
convenient and efficient to use.
 Database Applications:
 Banking: all transactions
 Airlines: reservations, schedules
 Universities: registration, grades
 Sales: customers, products, purchases
 Manufacturing: production, inventory, orders, supply chain
 Human resources: employee records, salaries, tax
deductions
 Databases touch all aspects of our lives
Purpose of Database System
 In the early days, database applications were
built on top of file systems
 Drawbacks of using file systems to store data:
 Data redundancy and inconsistency
 Multiple file formats, duplication of information in
different files
 Difficulty in accessing data
 Need to write a new program to carry out each new task
 Data isolation — multiple files and formats
 Integrity problems
 Integrity constraints (e.g. account balance > 0) become
part of program code
 Hard to add new constraints or change existing ones
Purpose of Database Systems (Cont.)
 Drawbacks of using file systems (cont.)
 Atomicity of updates
 Failures may leave database in an inconsistent state with
partial updates carried out
 E.g. transfer of funds from one account to another should
either complete or not happen at all
 Concurrent access by multiple users
 Concurrent accessed needed for performance
 Uncontrolled concurrent accesses can lead to inconsistencies
 E.g. two people reading a balance and updating it at the same
time
 Security problems
 Database systems offer solutions to all the above
problems
Levels of Abstraction
 Physical level describes how a record (e.g.,
customer) is stored.
 Logical level: describes data stored in
database, and the relationships among the
data.
type customer = record
name : string;
street : string;
city : integer;
end;
 View level: application programs hide details
of data types. Views can also hide information
(e.g., salary) for security purposes.
View of Data
An architecture for a database system
Instances and Schemas
 Similar to types and variables in programming languages
 Schema – the logical structure of the database
 e.g., the database consists of information about a set of customers and
accounts and the relationship between them)
 Analogous to type information of a variable in a program
 Physical schema: database design at the physical level
 Logical schema: database design at the logical level
 Instance – the actual content of the database at a particular
point in time
 Analogous to the value of a variable
 Physical Data Independence – the ability to modify the
physical schema without changing the logical schema
 Applications depend on the logical schema
 In general, the interfaces between the various levels and components
should be well defined so that changes in some parts do not seriously
influence others.
Data Models
 A collection of tools for describing
 data
 data relationships
 data semantics
 data constraints
 Entity-Relationship model
 Relational model
 Other models:
 object-oriented model
 semi-structured data models
 Older models: network model and
hierarchical model
Entity-Relationship Model

Example of schema in the entity-relationship model

Entity Relationship Model (Cont.)
 E-R model of real world
 Entities (objects)
 E.g. customers, accounts, bank branch
 Relationships between entities
 E.g. Account A-101 is held by customer Johnson
 Relationship set depositor associates customers with
accounts
 Widely used for database design
 Database design in E-R model usually converted to
design in the relational model (coming up next)
which is used for storage and processing
Relational
Model
 Example of tabular data in the relational model
Attributes

customer- customer- customer- account-

Customer-id
name street city number

192-83-7465 Johnson
Alma Palo Alto A-101
019-28-3746 Smith
North Rye A-215
192-83-7465 Johnson
Alma Palo Alto A-201
321-12-3123 Jones
Main Harrison A-217
019-28-3746 Smith
North Rye A-201
A Sample Relational Database
Data Definition Language (DDL)
 Specification notation for defining the database schema
 E.g.
create table account (
account-number char(10),
balance integer)
 DDL compiler generates a set of tables stored in a data
dictionary
 Data dictionary contains metadata (i.e., data about
data)
 database schema
 Data storage and definition language
 language in which the storage structure and access methods used
by the database system are specified
 Usually an extension of the data definition language
Data Manipulation Language (DML)
 Language for accessing and manipulating the
data organized by the appropriate data model
 DML also known as query language
 Two classes of languages
 Procedural – user specifies what data is required
and how to get those data
 Nonprocedural – user specifies what data is
required without specifying how to get those data
 SQL is the most widely used query language
SQL
 SQL: widely used non-procedural language
 E.g. find the name of the customer with customer-id 192-83-
7465
select customer.customer-name
from customer
where customer.customer-id = ‘192-83-7465’
 E.g. find the balances of all accounts held by the customer with
customer-id 192-83-7465
select account.balance
from depositor, account
where depositor.customer-id = ‘192-83-7465’ and
depositor.account-number =
account.account-number
 Application programs generally access databases through
one of
 Language extensions to allow embedded SQL
 Application program interface (e.g. ODBC/JDBC) which allow SQL
queries to be sent to a database
Database Users
 Users are differentiated by the way they expect to
interact with the system
 Application programmers – interact with system
through DML calls
 Sophisticated users – form requests in a database
query language
 Specialized users – write specialized database
applications that do not fit into the traditional data
processing framework
 Naïve users – invoke one of the permanent application
programs that have been written previously
 E.g. people accessing database over the web, bank tellers,
clerical staff
Database Administrator

 Coordinates all the activities of the database

system; the database administrator has a good
understanding of the enterprise’s information
resources and needs.
 Database administrator's duties include:
 Schema definition
 Storage structure and access method definition
 Schema and physical organization modification
 Granting user authority to access the database
 Specifying integrity constraints
 Acting as liaison with users
 Monitoring performance and responding to changes in
requirements
Transaction Management
 A transaction is a collection of operations that
performs a single logical function in a
database application
 Transaction-management component ensures
that the database remains in a consistent
(correct) state despite system failures (e.g.,
power failures and operating system crashes)
and transaction failures.
 Concurrency-control manager controls the
interaction among the concurrent
transactions, to ensure the consistency of the
database.
Storage Management
 Storage manager is a program module that
provides the interface between the low-level
data stored in the database and the
application programs and queries submitted
to the system.
 The storage manager is responsible to the
following tasks:
 interaction with the file manager
 efficient storing, retrieving and updating of data
Overall System Structure
Application Architectures

 Two-tier architecture: E.g. client programs using ODBC/JDBC to

communicate with a database
 Three-tier architecture: E.g. web-based applications, and
applications built using “middleware”
Types of DBMS
 Centralized Database
 Distributed Database
 NoSQL Database
 Relational Database
 Hierarchical Database
 Network Database
 Object Oriented Database
 Cloud Database
Centralized Database
 It is the type of database that stores data at a
centralized system.
 It comforts the users to access the stored data from
different locations through several applications
 Advantages
 It has decreased the risk of data management
 Data consistency is maintained as it manages data in a
central repository
 Provides better data quality
 Less costly
 Disadvantages
 Depends on Server and this a bottleneck for the
application
Distributed Database
 Data are distributed among different database
systems of an organization
 Homogeneous DDB
 Heterogeneous DDB

 Advantages
 Modular development is possible
 One server failure will not affect the entire data
set
 Scalable and more efficient than centralized
Database
NoSQL Database
 Are the databases that do not use SQL as their primary
data access language.
 Graph database, network database, object database
and document database are common NoSQL Database
 Key- Value Storage
 It stores every single item as a key holding its value together
 Document-oriented Database
 Store data as jason like documents,
 Graph Database
 In graph like structres
 Column stores
 Stores in large columns together instead of storing in rows
Cloud Database
 Database where data is stored in a virtual
environment and executes over the cloud
computing platform .
 Provides various cloud computing servers
 Amazon Web Services (AWS)
 Microsoft Azure
 Snowflake